How to Use LangChain Output Parsers to Structure Large Language Models Responses

langchain_reservations.py

from typing import List

from dotenv import load_dotenv
from langchain.llms import OpenAI
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import PromptTemplate
from pydantic import BaseModel, Field

load_dotenv()

model_name = "text-davinci-003"
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)

class Reservation(BaseModel):
    date: str = Field(description="reservation date")
    time: str = Field(description="reservation time")
    party_size: int = Field(description="number of people")
    cuisine: str = Field(description="preferred cuisine")

parser = PydanticOutputParser(pydantic_object=Reservation)

reservation_template = '''
  Book us a nice table for two this Friday at 6:00 PM. 
  Choose any cuisine, it doesn't matter. Send the confirmation by email.

  Our location is: {query}

  Format instructions:
  {format_instructions}
'''

prompt = PromptTemplate(
    template=reservation_template,
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
) 

_input = prompt.format_prompt(query="San Francisco, CA")

output = model(_input.to_string())

reservation = parser.parse(output)

print(_input.to_string())

for parameter in reservation.__fields__:
    print(f"{parameter}: {reservation.__dict__[parameter]},  {type(reservation.__dict__[parameter])}")